Job description

ABOUT US FAR.AI far.ai is a non-profit AI research institute working to ensure advanced AI is safe and beneficial for everyone. Our mission is to facilitate breakthrough AI safety research, advance global understanding of AI risks and solutions, and foster a coordinated global response. Since our founding in July 2022, we've grown to 50+ staff far.ai, published 40+ academic papers scholar.google.com, and convened leading AI safety events far.ai. Our work is recognized globally, with publications at premier venues such as NeurIPS, ICML including a Best Paper Honorable Mention in 2026 icml.cc, and ICLR, and features in the Financial Times ft.com, Nature News nature.com, Wired Magazine wired.com and MIT Technology Review technologyreview.com. We conduct pre-deployment testing on behalf of frontier developers such as OpenAI, independent evaluations for governments including the EU AI Office far.ai, and publish the AI Security Leaderboard leaderboard.far.ai based on our red-teaming expertise. We help steer and grow the AI safety field through developing arxiv.org research arxiv.org roadmaps researchgate.net with renowned researchers such as Yoshua Bengio; running FAR.Labs far.ai, an AI safety-focused co-working space in Berkeley housing 40+ members; and supporting the community through targeted grants far.ai to technical researchers. ABOUT FAR.RESEARCH We explore promising research directions in AI safety and scale up only those showing a high potential for impact. Once the core research problems are solved, we work to scale them to a minimum viable prototype, demonstrating their validity to AI companies and governments to drive adoption. We are aiming to rapidly grow our team in the following areas: - Mitigating AI deception: Studying when lie detectors induce honesty or evasion far.ai, and developing for deception and sandbagging - Evals and red-teaming: Conducting pre- and post-release adversarial evaluations of frontier models (e.g. Claude 4 Opus x.com, ChatGPT Agent cdn.openai.com, GPT-5 cdn.openai.com); developing novel attacks far.ai to support this work; and exploring new threat models (e.g. persuasion arxiv.org, tampering risks arxiv.org). - Infrastructure: Maintaining GPU compute infrastructure to support experiments with open-weight models and developing new tooling to allow our research teams to scale their fine-tuning and post-training workflows to frontier open-weight models. - Adversarial Robustness: Working to rigorously solve these security problems through building a science of security and robustness for AI, from demonstrating superhuman systems can be vulnerable far.ai, to scaling laws for robustness far.ai and jailbreaking constitutional classifiers arxiv.org - Mechanistic Interpretability: Finding arxiv.org issues arxiv.org with arxiv.org Sparse Autoencoders, probing deception using AmongUs arxiv.org, understanding learned planning far.ai in SokoBan and interpretable data attribution. FAR.AI far.ai is one of the largest independent AI safety research institutes, and is rapidly growing with the goal of diversifying and deepening our research portfolio. We would welcome the opportunity to add new research directions if you are a senior researcher with a strong vision and would like to pitch us on it. ABOUT THE ROLE This role would be a good fit for an experienced machine learning engineer, or an experienced software engineer looking to transition to AI safety research. All candidates are expected to: - Have significant software engineering experience. Evidence of this may include prior work experience and open-source contributions. - Be fluent working in Python. - Be results-oriented and motivated by impactful research. - Bring prior experience mentoring other engineers or scientists in engineering skills. Additionally, candidates are expected to bring expertise in one of the following areas corresponding to the core competencies our different research teams most need: - Option 1 – Machine Learning: - Substantial experience training transformers with common ML frameworks like PyTorch or jax. - Good knowledge of basic linear algebra, calculus, vector probability, and statistics. - Option 2 – High-Performance Computing: - Power user of cluster orchestrators such as Kubernetes (preferred) or SLURM - Experience building high-performance distributed-systems (e.g. multi-node training, large-scale numerical computation) - Experience optimizing and profiling code (ideally including on GPU, e.g. CUDA kernels). - Option 3 – Technical Leadership: - Experience designing large-scale software systems, whether as an architect in greenfield software development or leading a major refactor. - Comfortable project managing small teams, such as chairing stand-ups and developing detailed roadmaps to execute on a 3-6 month research vision. ABOUT THE PROJECTS As a Member of Technical Staff (Senior Research Engineer) you would join one of our existing workstreams and lead projects there: - Detecting and preventing deception. Under what conditions can we reliably detect deceptive behaviour from models, and can such behaviour be effectively mitigated at scale? This would focus on large-scale training of transformers. - Preventing catastrophic misuse. Apply our research insights to detect and mitigate vulnerabilities and other risks in frontier AI models. This would focus more on technical leadership - Accelerating our research. Build frameworks and infrastructure that allows us to ask bigger questions and more rapidly run new experiments, to deepen our research. This would focus more on high-performance computing. As we continue to grow our research portfolio, additional workstreams may open up for contribution, for example in mechanistic interpretability. LOGISTICS If based in the USA or Singapore, you will be an employee of FAR.AI far.ai (501(c)(3) research non-profit / non-profit CLG). Outside the USA or Singapore, you will be employed via an EOR organisation on behalf of FAR.AI far.ai or as a contractor. - Location: Both remote and in-person (Berkeley, CA or Singapore) are possible. We sponsor visas for in-person employees, and can hire remotely in most countries. - Hours: Full-time (40 hours/week). - Compensation: $225,000 to $350,000 per year. Compensation is determined by our salary bands and your level of experience, with adjustments for location. We also pay for work-related travel and equipment expenses, and offer catered lunch and dinner at our office in Berkeley. - Application process: A 72-minute programming assessment, two interviews with members of our technical staff, and a 1-2 week paid work trial. If you are not available for a work trial we may be able to find alternative ways of testing your fit. If you have any questions about the role, feel free to contact us at talent@far.ai. Otherwise, if you don't have questions, the best way to ensure a proper review of your skills and qualifications is by applying directly via the application form. Please don't email us to share your resume (it won't have any impact on our decision). Thank you!

Requirements

Must-haves

This role would be a good fit for an experienced machine learning engineer, or an experienced software engineer looking to transition to AI safety research. All candidates are expected to:
Have significant software engineering experience. Evidence of this may include prior work experience and open-source contributions.
Be fluent working in Python.
Be results-oriented and motivated by impactful research.
Bring prior experience mentoring other engineers or scientists in engineering skills.
Additionally, candidates are expected to bring expertise in one of the following areas corresponding to the core competencies our different research teams most need:
Substantial experience training transformers with common ML frameworks like PyTorch or jax.
Good knowledge of basic linear algebra, calculus, vector probability, and statistics.
Experience building high-performance distributed-systems (e.g. multi-node training, large-scale numerical computation)
Experience optimizing and profiling code (ideally including on GPU, e.g. CUDA kernels).
Experience designing large-scale software systems, whether as an architect in greenfield software development or leading a major refactor.
Comfortable project managing small teams, such as chairing stand-ups and developing detailed roadmaps to execute on a 3-6 month research vision.
Location: Both remote and in-person (Berkeley, CA or Singapore) are possible. We sponsor visas for in-person employees, and can hire remotely in most countries.
Hours: Full-time (40 hours/week).

Nice-to-haves

Power user of cluster orchestrators such as Kubernetes (preferred) or SLURM

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigomachine learningpythonmlkubernetes

Platform: ashby
Effort: quick

Benefits

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Vision